B2B Data Analytics: Transform Data into Predictable Growth

Modern B2B data analytics is the practice of turning streams of B2B transaction and customer data into actionable insights businesses can use to predict and drive growth.

The problem with B2B data analytics today is that data is plentiful, but analysis—good, practical analysis—is not. In the second quarter of 2025 alone, US consumers generated an estimated $304.2 billion in ecommerce sales, according to the US Census Bureau, creating massive amounts of data. But we’re long past the days of “big data” being an exciting keyword, or “data is the new oil” as an adage that inspires transformation. Every tool produces data, and every platform promises insight—the challenge is turning it into information you can use. 

That challenge is also an opportunity. Modern businesses need real-time, unified commerce analytics that bridge B2B and DTC operations to drive predictable growth. The winners in B2B aren’t those who simply generate reports, but those who transform analytics into a forward-looking growth engine that powers personalization, predicts buyer behavior, and accelerates decision-making across channels. 

What is B2B data analytics?

B2B data analytics refers to the processes and tools an enterprise uses to collect, integrate, and analyze data from transactions and interactions between businesses. This includes data from sales to other companies, such as wholesale orders and enterprise contracts, as well as related operations, such as supply chain management and customer service. 

In modern commerce, B2B analytics spans both online and offline channels to give a complete picture of business operations—especially when paired with the right mix of B2B products and services that feed data into a single view. The focus is on account-level insights, longer sales cycles, and multi-person buying processes, which differ from the volume-based nature of many B2C sales operations. Just as important, enterprises now need unified analytics across all channels to understand their customers fully and make decisions with a single source of truth.

From static reports to predictive intelligence

Historically, B2B analytics has been limited. Often, it simply meant generating periodic reports, such as monthly sales by region or quarterly revenue by product line, and these reports were primarily intended to inform leaders about why something had happened, long after it had happened.

Today, B2B analytics has matured into a field unto itself that includes real-time dashboards and algorithms that use hindsight to provide foresight. Modern B2B analytics systems can automatically flag anomalies in sales data, predict when a major client is likely to reorder, help sales teams recommend products to a buyer based on purchase patterns, and more. 

B2B analytics can learn from B2C analytics, but copying tactics directly rarely works. B2B and B2C companies share some common goals, but the focus and scale differ. 

B2C analytics deals with high volumes of individual customers and transactions. In contrast, B2B analytics deals with lower volumes but higher complexity. This means there are typically fewer customers with large order values and longer, more involved sales cycles. A single B2B client might involve a buying committee of 10 or more stakeholders, all influencing one large deal, and that one large deal might be the difference between a good quarter and a bad one. 

For example, AMR Hair & Beauty, one of Australia's leading hair and beauty suppliers, sells to both businesses and consumers across physical and online stores. By adopting Shopify Plus, the brand saw a 77% rise in B2B average order value. “We’re tracking the performance of our website, and based on that we will tweak the cart/checkout page. We couldn’t do that with our old platform,” says Ammar Issa, founder of AMR Hair & Beauty.

Industry momentum reflects this shift. McKinsey research shows that 64% of B2B companies “expect to increase their investments in predictive analytics.” Companies have recognized that data alone isn’t enough. It’s the intelligence derived from data that matters, and that intelligence is often dependent on the platform underlying your analytics.

The strategic value of B2B data analytics

Treating analytics as just another IT project sets the bar too low. When done right, analytics delivers value on multiple fronts: accelerating revenue growth, boosting operational efficiency, and elevating customer experience. 

At its best, data-driven decision-making in B2B can have a compounding effect across the enterprise. McKinsey research shows, for example, that B2B “outperformers” that excel in data and analytics have been shown to significantly outperform peers—to the tune of 15%–25% higher earnings before interest, taxes, depreciation, and amortization (EBITDA). 

Revenue acceleration through data insights

By understanding your best customers, anticipating their needs, and pricing intelligently, you can create a virtuous cycle that turns data into revenue, one that accelerates the more you learn.

• Identify high-value customer segments: Analytics helps pinpoint which customers or segments contribute the most to your revenue and profit, and which have the highest growth potential. By focusing on the right segments, companies can find niches or account types that yield outsized returns.
• Predict reorder patterns and timing: Many B2B businesses rely on recurring orders, and analytics can uncover each account’s buying patterns and build models to predict when they will reorder and what they are likely to need. 
• Optimizing pricing strategies across channels: Analytics can uncover patterns to optimize pricing for profitability and competitiveness. Insights from a B2B pricing strategy framework, for example, can help identify where customers are less price-sensitive, indicating room to increase margin.
• Forecast sales outcomes: More advanced predictive analytics can even estimate deal-close probability or flag accounts at risk of churn, giving sales teams a headstart.

Analytics also drives revenue acceleration. Decathlon, the world's largest sporting goods retailer, adopted Shopify Plus and sped up reporting by 50% and 60% faster data analysis through ready-to-use reporting templates.

“We can easily see year-over-year numbers on a graph and combine multiple key performance indicators (KPI) into one report, which is very useful,” says Tony Leon, chief technology officer for Decathlon USA. “We can also easily keep track of peaks or drops in sales and quickly compare numbers from one timeframe to the other.”

Operational efficiency gains

Beyond top-line growth, B2B analytics can deliver significant efficiency improvements.

• Automate manual reporting processes: Modern analytics platforms can automate data collection and report generation, freeing up teams to focus on analysis rather than data preparation. Real-time dashboards replace weekly reports and cut manual errors.
• Real-time inventory and supply chain visibility: Analytics that provide immediate visibility into inventory levels across warehouses, stores, and in transit allow companies to maintain dynamic reallocation of stock, faster order routing, and less need for safety stock.
• Reducing cost to serve through self-service insights: In B2B, customers frequently have questions about their order history, invoices, and product information. An analytics portal or dashboard can allow customers to get answers on their own.

The cumulative effect of all these operational efficiency gains is a lower cost to serve for each customer, which directly boosts profitability. For example, DECKED, a manufacturer of truckbed storage and organization systems, illustrates this: by using Shopify to capture live data, the company streamlined operations and fueled growth.

“Shopify is huge for us, especially for real-time data,” says Ashlee Weber, director of ecommerce and performance marketing at DECKED. “We're able to look in real time, whereas some of the other platforms don't let us do that. We have that opportunity in Shopify to make decisions.”

Customer experience transformation

In the past, customer experience wasn’t always the top priority in B2B. Sales were relationship-driven, and buyers accepted some friction, such as phone orders and lengthy quotes, as the cost of doing business. Not anymore. Modern B2B buyers demand a convenient, personalized, and even enjoyable experience on par with consumer shopping. 

• Personalization at scale for B2B buyers: B2B clients, like B2C customers, respond better to experiences tailored to their needs. The challenge is that in B2B, you must personalize at the account or segment level and often across multiple touchpoints—something that’s only possible with unified analytics.
• Predictive recommendations based on purchase history: B2B recommendations often require purchase history, product usage data, and knowledge of business context. Predictive recommendations can drive cross-sell and upsell, boosting average order value (AOV).
• Proactive customer success management: Instead of waiting for a client to complain or, worse, to churn, analytics allow you to use data signals to intervene early. B2B businesses often derive a large portion of revenue from repeat orders or long-term contracts, so improving retention can dramatically raise profits. 

Customer experience improves most when processes become seamless. For example, Future Glass, the B2B arm of Glass Warehouse, adopted Shopify B2B to reduce manual effort and deliver custom catalogs with unique pricing for each customer. The result: a 340% increase in B2B sales and 83% lift in conversions. 

According to Parker Vitek, Glass Warehouse’s content manager, customers can “just go through the checkout process, select their terms, and go on with their day. Because of this new efficiency, we’re getting 90% of our orders out the same day.”

Essential B2B analytics capabilities for enterprise

The strategic value described above is possible for all B2B companies, but capturing this value isn’t a matter of flipping a switch or adopting a tool. To build a B2B analytics engine, enterprises need to develop core analytics capabilities that, when put together, allow them to drive the growth they need. 

Customer behavior analytics

Understanding your B2B customers’ behavior is foundational. In B2B, unlike in B2C, this often means account-level behavior over a longer journey.

• Account-level purchasing patterns: Track and analyze how each business customer buys over time. What products do they buy the most? How often do they place orders? What’s their average order value? Are they increasing or decreasing spend? By analyzing these patterns, you can categorize accounts and tailor strategies to each. 

• Multi-stakeholder journey mapping: B2B purchase decisions often involve multiple stakeholders, resulting in a nonlinear and lengthy buying journey. Mapping this B2B customer journey with analytics reveals which touchpoints matter most for conversion.
• Cross-channel attribution modeling: B2B marketing and sales occur over many channels, including digital ads, webinars, white papers, in-person events, sales visits, emails, and more. A robust analytics capability will use models, such as first-touch, last-touch, and multi-touch attribution, and even algorithmic attribution, to assign credit. 

With this level of customer behavior analytics, you move from guessing how your business clients behave to knowing and predicting. 

Sales performance analytics

Sales performance analytics focuses on the metrics and insights that help you manage and improve your sales organization. In enterprise B2B, where sales cycles are long and teams are large, having data on sales activities and outcomes is crucial for hitting targets.

• Pipeline velocity and conversion metrics: Track opportunities as they move through stages (lead, qualified, proposal, negotiation, closed, etc.) and calculate metrics like conversion rates and velocity, which you can use to identify bottlenecks.
• Account penetration analysis: Account penetration analytics looks at how deeply you have penetrated each account relative to its potential using metrics like product adoption, geographic or department penetration, and revenue trends per account.
• Sales team productivity dashboards: Productivity analytics looks at indicators like the number of calls and meetings per rep, proposals sent, win rates per rep, average deal size, quota attainment, and sales cycle length by rep or team. By presenting these in dashboards, sales leaders can identify where to improve.

The result of these efforts is a more predictable sales engine, one that allows you to forecast results more accurately and know where to pull levers to boost sales outcomes. 

Inventory and supply chain analytics

For product-based B2B companies, inventory and supply chain analytics are critical. The ability to meet customer demand efficiently without tying up excess capital can be the difference between a profitable month and a wasteful one.

• Real-time stock level visibility: Analytics that provide an accurate , consolidated view of stock levels for each product across all locations enable companies to confidently commit to customers because they know exactly what’s available and where. 
• Demand forecasting by customer segment: B2B demand can be hard to predict. By forecasting at a segment level, you can build a nuanced plan that incorporates seasonality, trends, and even external market factors to predict future demand. 
• Automated reorder-point optimization: Instead of relying on rules of thumb, advanced analytics can set reorder points based on actual demand variability and lead times. This allows companies to consider the service level they want to achieve and the volatility of demand. 

Dalfilo, for example, an Italian artisanal home linen brand, used Shopify to simplify inventory management and logistics, allowing them to optimize operations across all sales channels while streamlining formerly tricky backend processes. The company ultimately achieved a 1,000% increase in business within four years.

Building your B2B analytics strategy

Building a B2B analytics strategy involves assessing your current position, defining your desired outcome (and how you’ll measure success), and selecting the right technology and approach to achieve it. With fragmented tools and a disorganized path to your goal, you risk spending a lot of time with little to show for it. 

Assess your current data maturity

The first step is a clear-eyed accounting of your current data and analytics maturity. Overestimating maturity at this stage can push you off track.

• Data collection audit checklist: Catalog all the data sources you currently have and what data is being collected (or not). Include sales transaction data, marketing data, customer data, product data, and any external data you use. For each source, evaluate data quality and accessibility.
• Technology stack evaluation: Next, review the tools and systems in your current analytics stack. Similarly, look at your major enterprise systems (ERP, CRM, and ecommerce platform) to see what native analytics capabilities they have and how well they integrate. 
• Skills gap analysis: Assess the skills of your team related to data. Do you have data analysts or data scientists on staff? Are business users data-literate? If you plan to use advanced analytics, do you have the expertise to develop and maintain those, or will you need to hire or upskill? Identify where training might be required. 

After this assessment, you should have a clear picture of your starting point and a baseline for further iteration and experimentation. 

Define success metrics and KPIs

Like any strategic initiative, your B2B analytics program needs clear goals and key performance indicators (KPIs) to measure success. It’s not enough to “do analytics.” You want to use those analytics to drive specific, measurable business outcomes. 

• Revenue-focused metrics: Identify the top-line metrics you want to influence with analytics. This could be overall B2B revenue growth percentage, or more granular metrics like conversion rate from lead to customer, average order value, and customer lifetime value (LTV) for your B2B clients. 
• Efficiency indicators: Determine which operational efficiency metrics you want to improve. This might involve cost and productivity measures, such as order-processing cost per order or inventory turnover. 
• Customer satisfaction scores: If a goal of your analytics initiative, through better personalization or service, is to improve the customer experience, set a target for scores across metrics like Net Promoter Score (NPS), customer satisfaction (CSAT), and customer effort score (for ease of doing business). 

Dermalogica, for example, used Shopify to unify their back end and create a buying experience for its B2B customers that led to a 3x increase in reorder frequency and a 23% increase in conversion rate, and 75% of customers rated the buying experience as a 4 out of 5 or higher.

Select the right analytics platform

With a clear understanding of where you stand and what you want to achieve, the next step is choosing the right platform to execute your B2B analytics strategy. This is a critical decision: the platform will be the layer that enables or limits all the capabilities covered so far.

• Build vs. buy considerations: In an enterprise, “build” might mean using internal data engineers to craft a data warehouse, adopting open-source analytics frameworks, and tailoring everything. “Buy” might mean adopting a proven platform that a vendor offers. Each approach has pros and cons, but buying tends to get you up and running faster with best practices built in. 
• Integration requirements: The platform you choose must be able to integrate with all your key data sources, including your CRM, ERP, marketing automation, and more. A good platform will be modular and integration-friendly, offering prebuilt connectors to common enterprise apps as well as customization options. 
• Scalability planning: The platform you choose should scale with you, not limit you. Scalability has several dimensions: data volume, concurrent users, and analytical complexity. For most enterprises processing millions of transactions and multiple business units, erring on the side of scalability is smart.

Dollar Shave Club, for example, experienced both sides of the build-or-buy decision, and found themselves happiest with Shopify. When the company migrated from a homegrown platform to Shopify, they:

• Decreased tech maintenance resources by 40%
• Reached new global audiences of 100 million users through the Shop app
• Transitioned their international sites over to Shopify in weeks instead of months

Kyle Iwamoto, vice president of ecommerce at Dollar Shave Club, says, “We spent about 40% of our total tech resources just on maintaining our homegrown platform.” 

Implementation roadmap for enterprise B2B analytics

Implementing B2B analytics at an enterprise scale is best approached in phases. This allows you to deliver value quickly, learn and iterate, and manage change in practical chunks. Here, we’ve broken a model roadmap into three phases—Foundation, Expansion, and Optimization—but the timelines are illustrative of the approach, not an exact timetable. 

Phase 1: Foundation (Months 1–3)

In the first phase, focus on building a strong foundation for your long-term analytics initiative. This is where you set up the core infrastructure and governance needed for success, and score some quick wins to prove value.

• Data governance framework: Start by establishing policies, roles, and processes around your data. This includes defining data ownership, creating data definitions and a business glossary, and implementing security and access controls. If operating globally, ensure compliance with regulations like GDPR for EU customer data, too. 
• System integration planning: Map out how data will flow from source systems (ERP, CRM, ecommerce, etc.) into your analytics platform. This involves configuring connectors and setting up ETL/ELT pipelines. The key is to create a single source of truth. 
• Quick win identification: Identify a few high-impact, low-complexity analytics use cases you can implement quickly. For example, implementing a simple sales dashboard for management that aggregates data everyone had trouble pulling before. 

With your foundation laid, including confidence from stakeholders in your foundation, you can set off to the next phase. 

Phase 2: Expansion (Months 4–6)

In the second phase, you expand and deepen your analytics implementation. With the groundwork done, you can roll out more advanced analytics features and extend to more users and data domains.

• Advanced analytics rollout: This is when you can start introducing sophisticated analytics capabilities that go beyond basic dashboards. These could include deploying predictive models, advanced segmentation, or complex scenario analysis. 
• Team training and adoption: Conduct training sessions for all user groups. Sales managers need to know how to use the new pipeline dashboard, marketers need to understand the attribution reports, and operations people need to navigate inventory analytics. The goal is to drive adoption. 
• Process optimization: Phase 2 is an ideal time to refine processes based on the data insights from Phase 1. For example, if the data shows frequent stockouts for certain products, you might implement a new inventory planning process to address it. Let the data reveal inefficiencies or bottlenecks, and then adjust operations accordingly.

By the end of Phase 2, you should have enterprise-wide analytics in active use, including core dashboards for key departments, some predictive models or advanced features running, and a workforce that is getting comfortable using data in daily decisions. 

Phase 3: Optimization (Months 7+)

In Phase 1, you built a foundation and deployed pilots; in Phase 2, you expanded to a broader deployment; in Phase 3, you can get truly advanced, allowing you to fine-tune and iterate on the system long-term.

• AI and machine learning (ML) implementation: Phase 3 is where you can fully integrate AI and sophisticated ML models into your operations. This could include deploying things like predictive maintenance models, advanced price-optimization algorithms, or AI-driven chatbots for customer service. 
• Predictive model refinement: Any predictive models or algorithms you introduced in earlier phases should be refined continuously. Phase 3 involves iterating on models with new data, improving accuracy, and expanding their scope.
• Continuous improvement cycles: Finally, Phase 3 calls for embedding a culture of continuous improvement for analytics. Technology and business conditions will change, so your analytics strategy needs to keep evolving. Set up regular review meetings, check KPI progress, monitor user adoption levels, and identify new opportunities. 

By the end of Phase 3 (and onward), your enterprise B2B analytics should be mature and driving continuous value. You’ll have AI-enhanced capabilities, very high user adoption, and a culture that routinely looks to data for answers. 

Overcoming common B2B analytics challenges

We’ve laid out a simple path to success, but a simple path isn’t necessarily an easy one. By learning the typical challenges ahead of time, you can plan mitigations as part of your strategy. 

• Data silos across systems: Enterprises often suffer from fragmented data and silos that prevent a unified view. Overcoming this is your top priority. The solution involves both technology and governance. Platforms that enable real-time, unified analytics make the process far easier.
• Resistance to change: Employees may be used to doing things “the old way” and might distrust or underutilize new analytics tools. Overcoming this requires strong change-management practices, including clear communication of why the change is happening, training and support, and involving users in the design so they have ownership. 
• Technical integration complexity: Even with good planning, integrating multiple enterprise systems and ensuring data pipelines run smoothly can be complex. Mitigating this involves having skilled data engineers or integration partners who can troubleshoot and build pipelines. Choosing a platform known for easy integration, such as Shopify, can make this easier. 
• ROI-measurement difficulties: Ironically, even after implementing analytics, some companies struggle to quantify the ROI of analytics itself. The value can be diffuse or take time to materialize. To ensure you justify the investment, plan upfront how to measure ROI, including quantifiable KPIs and metrics, as well as qualitative workflow improvements. 

When Schleich, a German toy manufacturer, adopted Shopify, they achieved a 31% improvement in checkout abandonment rate and a 25% increase in orders. Shopify Analytics allowed the company to analyze store data and generate reports much more quickly while also integrating with the third-party systems they needed. “The ease of integrations in all directions saves us an incredible amount of time,” says Alexander Wahl, head of digital commercial enablement at Schleich.

The future of B2B data analytics

B2B data analytics is on the verge of becoming a make-or-break capability. As AI data-analysis matures, prepared companies will rapidly outpace those still building pre-AI analytics suites.

Gartner research predicts that, by 2027:

• 50% of business decisions will be augmented or automated by AI agents for decision intelligence. 
• Organizations that emphasize AI literacy for executives will achieve 20% higher financial performance compared with those that do not.
• Organizations that prioritize semantics in AI-ready data will increase their GenAI model accuracy by up to 80% and reduce costs by up to 60%.

Looking ahead, two shifts will be especially important for enterprises:

• Unified commerce intelligence: Enterprises will demand one analytics layer that brings together B2B and DTC operations. A unified view of customers, inventory, and profitability will be essential for accurate predictions and consistent decision-making across channels.
• Self-serve customer analytics: B2B buyers will increasingly expect predictive dashboards and account-level insights they can access themselves. From tracking spend to forecasting reorders, self-serve analytics will become part of the buying experience.

Enterprises that prepare now will turn analytics into a lasting advantage.